Small Business MCP Server

A Model Context Protocol (MCP) server that connects language models with small business data systems. This server implements the MCP paradigm to create a structured, context-aware interface between LLMs and business information, providing tools for data manipulation, contextual resources, and interactive prompt templates.

MCP Primitives

This server implements all three core MCP primitives:

Primitive	Control	Description	Implementation
Tools	Model-controlled	Functions for LLMs to take actions on data	Document processing, data queries
Resources	Application-controlled	Contextual data for LLM context	Business documents, database schema
Prompts	User-controlled	Interactive templates for common operations	Report generation, data analysis

Features

• Structured Model Context

  • Exposes business data through MCP resources
  • Provides contextual information to improve model reasoning
  • Maintains consistent representation of business concepts
  • Enables precise knowledge retrieval

• AI-Powered Operations

  • Model-controlled tools for data manipulation
  • File content extraction and categorization
  • Automated relationship mapping between entities
  • Version-controlled document management

• Interactive Business Intelligence

  • Natural language querying of business data
  • Templated prompts for common business tasks
  • Customizable report generation
  • Contextual analysis of business metrics

Requirements

• Python 3.8+
• PostgreSQL 12+
• Required Python packages:
  • mcp (Model Context Protocol SDK)
  • psycopg2 (PostgreSQL adapter)
  • PyPDF2 (PDF processing)
  • Pillow (Image processing)
  • python-magic (File type detection)
  • langchain (NLP processing)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/small-business-mcp.git
   cd small-business-mcp
   

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Set up PostgreSQL:

   # Create database
   createdb small_business_db
   
   # Set environment variables
   export DB_HOST=localhost
   export DB_PORT=5432
   export DB_NAME=small_business_db
   export DB_USER=your_username
   export DB_PASSWORD=your_password
   

Usage

The MCP server acts as a bridge between language models and your business systems, structured around the three core MCP primitives (tools, resources, and prompts). This architecture allows LLMs to:

1. Retrieve contextual information about your business via resources
2. Take actions on your business data via tools
3. Leverage specialized templates for common tasks via prompts

Starting the Server

You can start the MCP server using either of these methods:

# Method 1: Direct Python execution
python -m MCPServer.server

# Method 2: Using MCP CLI
mcp run MCPServer/server/server.py

The server uses FastMCP to expose tools, resources, and prompts to LLMs.

Connecting with a Client

Connect to the server using the MCP client SDK:

import asyncio
from mcp import ClientSession, StdioServerParameters

async def main():
    # Create server parameters for connecting to the MCP server
    server_params = StdioServerParameters(
        command="python",
        args=["-m", "MCPServer.server"],
        env=None,  # Optional environment variables
    )
    
    async with ClientSession(server_params) as session:
        # Initialize connection
        await session.initialize()
        
        # List available tools
        tools = await session.list_tools()
        print(f"Available tools: {[tool.name for tool in tools]}")
        
        # Process a directory
        result = await session.call_tool(
            "process_business_directory",
            arguments={"directory_path": "/path/to/business/files"}
        )
        print(f"Directory processing result: {result}")
        
        # Query business data
        query_result = await session.call_tool(
            "query_business_data",
            arguments={"query": "Show me all PDF documents from 2023"}
        )
        print(f"Query result: {query_result}")

if __name__ == "__main__":
    asyncio.run(main())

Mounting to an Existing ASGI Server

You can integrate the MCP server with existing ASGI applications:

from starlette.applications import Starlette
from starlette.routing import Mount
from MCPServer.server.server import mcp  # Import the FastMCP instance

# Mount the MCP server to an existing ASGI server
app = Starlette(
    routes=[
        Mount('/mcp', app=mcp.sse_app()),
    ]
)

Available MCP Primitives

MCP defines a structured way for LLMs to interact with external systems through three core primitives, each with a distinct control model and purpose. This server implements:

Tools

Models can use these tools to interact with business data:

• process_business_directory: Import and organize files from a directory
• analyze_document: Process a specific document and extract its content/metadata
• query_business_data: Natural language query interface for business data
• store_chat_history: Record conversation history in the database
• create_business_report: Generate reports based on stored data

Resources

Resources provide contextual information to LLMs, exposing data that helps models reason about business objects. Unlike tools, resources are passively accessed rather than executed:

• schema://database: Database schema for reasoning about data structure
• doc://{document_id}: Access to specific document content
• stats://business: Business statistics and metrics

Prompts

Prompts are user-controlled templates that guide how LLMs approach specific tasks, providing a standardized way to frame common business operations:

• generate_report: Template for creating various business reports
• analyze_data: Template for analyzing business data patterns
• setup_assistant: Template for configuring a business data assistant

Server Capabilities

The server declares these capabilities during initialization:

Capability	Feature Flag	Description
tools	listChanged	Business data tools
resources	subscribelistChanged	Document and schema access
prompts	listChanged	Business report templates
logging	-	Server logging configuration
completion	-	Argument completion suggestions

Security

• All database credentials and API keys are managed through environment variables
• File access is restricted to specified directories
• Data encryption for sensitive information
• Regular backup procedures

Contributing

Contributions are welcome! Please read our Contributing Guidelines for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.